Locational Marginal Emission Rate Calculation Using Real-Time Market Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining pollutant emission reductions in electric power systems are inefficient, relying on system-average emissions rate estimation and marginal emissions rate estimation, which are laborious, costly, and inaccurate due to unobservable inputs, making it difficult to assess the contribution of specific clean power generators to CO2 emission reduction.
Innovation Solution
The system and method utilize nodal data from the Real-Time Market to calculate locational marginal carbon emission rates, avoiding the need for large-scale power system models and focusing on actual data from the market, allowing for precise determination of pollutant emissions at each location and the contribution of clean power generators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If aggregate CO2 emissions factor is used to calculate contribution of clean power generation, then calculation is simple, but accuracy of emission reduction assessment is poor
Solution Approach 1:
The patent segments the power system into multiple locations/nodes and calculates emission rates separately for each location rather than using a single aggregate system-wide factor. This allows accurate assessment of emission reductions at specific locations where clean power generators are situated, resolving the contradiction by dividing the system into manageable segments that can be analyzed individually.
Solution Approach 2:
The patent implements locational marginal emission rates that provide location-specific emission factors rather than uniform system-average factors. This enables accurate measurement of emission reductions at each specific location, allowing stakeholders to understand the local impact of clean power generation and make informed decisions about where to deploy clean energy resources.
2Measurement precision
If direct current optimal power flow sensitivity method is used to determine locational emissions rates, then location-specific data is obtained, but computational burden and cost are high
Solution Approach 1:
The patent uses real market data (injection shift factors, locational marginal prices, transmission constraint shadow prices) as a copy or representation of the actual power system operation, avoiding the need to solve complex DC-OPF sensitivity equations. This approach replicates the essential information needed for locational emission rate calculation from existing market data, significantly reducing computational burden while maintaining accuracy.
Solution Approach 2:
The patent transforms the calculation approach by changing from solving complex power flow equations to using directly observable market parameters. By utilizing real-time market data parameters that are already measured and reported, the system achieves locational emission rate calculation without the heavy computational burden of traditional methods.
3Loss of information
If system-average emissions rate is used, then data collection is simple, but contribution of specific clean power generators cannot be determined
Solution Approach 1:
The patent segments emission rate information by location, providing distinct emission factors for each node in the power system. This segmentation enables the tracking and attribution of emission reductions to specific clean power generators at specific locations, preserving information about individual generator contributions while using data that is already collected by market operators.
Data Source
AI summary
Systems, methods, and techniques are disclosed whereby locational marginal emission rates may be determined for each location (or node) of an electric power generation and distribution system using Real-Time Market data provided by a power system market operator, along with marginal offer estimate data and generator emission data. Noisy and incomplete input data can be appropriately handled to allow use of Real-Time Market data. The effect of congestion (violated constraints) can also be removed.